The field of image processing is moving towards developing more sophisticated and task-driven approaches to enhance and adapt images in various environments. Researchers are focusing on creating models that can effectively address challenges such as image degradation, blur, and domain shifts. The development of novel architectures and techniques, such as frequency-driven kernel prediction and adaptive cross-domain learning, is enabling significant improvements in image quality and accuracy. Noteworthy papers in this area include AquaFeat, which achieves state-of-the-art results in underwater object detection, and FOCUS, which proposes a frequency-based conditioning approach for mitigating catastrophic forgetting during test-time adaptation. Additionally, papers like MBMamba and AdaSFFuse are introducing innovative solutions for image deblurring and multimodal image fusion, respectively.